The commercial, military, and civilian unmanned aerial vehicle industry, commonly referred to as unmanned aerial vehicles, is an emerging industry in the United States estimated to be $7 to $10 billions of dollars by 2020. The commercial unmanned aerial vehicle market is expected to be between $2 to $3 Billion by 2020.
Commercial unmanned aerial vehicles have many potential applications especially in the areas of data collection, thus driving rapid innovation and variety of unmanned aerial vehicle hardware and software products. However, as unmanned aerial vehicle applications become mainstream businesses and consumers are seeking better ways to manage the increasingly diverse set of applications and the associated data collected by these unmanned aerial vehicles.
In particular, with the growing sophistication and potential applications for unmanned aerial vehicles there exist increase complexities of organizing and managing vast array of data collected by unmanned aerial vehicles such as video, pictures, and other measurements. Generally, the methods of managing unmanned aerial vehicle workflow are targeted at control and operations.
These can include fleet management, unmanned aerial vehicle flight path management, and data storage management. However, the existing repository for unmanned aerial vehicle generated results are rudimentary and generally lack automation correlating data sets with spatial information including time and positioning.
A current problem with the unmanned aerial vehicle manufacturers and third party software platforms is that they provide platforms for managing the unmanned aerial vehicle hardware, flight plans, and possible raw storage of the unmanned aerial vehicle data; however, currently there is limited ability to efficiently address unique workflow of correlating the unmanned aerial vehicle flight path data with the unmanned aerial vehicle data recordings to provide efficient data analytics and intuitive use. This results in fragmented and manual work for the end user to manage and process both the unmanned aerial vehicle flight data and the unmanned aerial vehicle data recordings.
The lack of standardized workflow data automation by existing vendors limits the potential effectiveness of the unmanned aerial vehicles. The workflow requires multiple manual steps relying on a user's intuition in order to visualize, extract, and correlate this data rather than on a set of concrete rules and procedures.
In addition, the process that an end user must employ is unique to each different type, class, or brand of unmanned aerial vehicle. Today no single automated workflow exists for users to visually synchronize the data collected by the unmanned aerial vehicle along with the unmanned aerial vehicle flight path data.
Solutions have been long sought but prior developments have not taught or suggested any complete solutions, and solutions to these problems have long eluded those skilled in the art. Thus, there remains a considerable need for a system that can efficiently and effectively synchronize flight path data with recorded data.